1. Trajectory Pattern Mining Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli KDD Lab (ISTI-CNR & Univ. Pisa) Presented by: Qiming Zou

2. Overview  Motivations  Trajectory  T-Pattern  Regions of Interest  Future Work  Q&A

3. Motivations  Large number of mobile devices, mobile services available

4. Motivations  It is possible to collect position traces from such devices  We can extract information and patterns from these data to describe mobility behaviors  Use this information for fields such as urban planning

5. Trajectory  Trajectories are sequences that contain the spatial and temporal information about movements

6. Trajectory  Trajectories are usually given as spatiotemporal (ST) sequences:  x i, y i is the position coordinate relative to the origin  t i is the time stamp for the position information

7. Trajectory  2D and 3D representation of a trajectory:

8. T-Pattern  A Trajectory Pattern (T-Pattern) is a couple (s, α), where: s = is a sequence of n+1 locations α= are the transition times such that α i = Δt i = t i – t i-1

9. T-Pattern  A T-Pattern T p occurs in a trajectory if it contains a subsequence S such that: each (x i, y i ) in T p matches a point (x i ’, y i ’ ) in S the transition times in T p are similar to those in S

10. T-Pattern  The same exact spatial location (x, y) usually never occurs Yet, close locations often represent the same place  The same exact transition times usually do not occur often However, close times often indicate similar behavior

11. T-Pattern  To solve the problem, we introduce the notions of: Spatial neighborhood: Two points match if one falls within a spatial neighborhood N() of the other Temporal tolerance: Two transition times match if their temporal difference is ≤ τ

12. T-Pattern  Example:

13. Regions of Interest  It is too computational intensive and yield little practical use to generate all T-Patterns  Solution: Use a Regions of Interest approach, only use these regions as nodes of the T-Patterns

14. Regions of Interest  Given a set of Regions of Interest R, define the neighborhood of (x, y) as: Neighbors = belong to the same region Points in no region have no neighbors

15. Regions of Interest S= =>

16. Regions of Interest  What if the Regions of Interests are not known before hand?  Define heuristics for automatic Regions of Interest extraction from data: Geography-based (crossroads) Usage-based (popular places) Mixed (popular squares)

17. Future Work  Application-oriented tests on large, real datasets  Study relations with Geographic background knowledge Privacy issues Reasoning on trajectories and patterns

18. Trajectory Pattern Mining Questions?